An Observable Experiment: Control Over the Variables

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Table of Contents

Introduction
Justification
Dependent Variables
Independent Variables
Factors/Levels
Experimental Units
From Observable to Designed
Reference List

Introduction

Setting an experiment is a crucial step toward a better understanding of a specific phenomenon (Groebner, Shannon, & Fry, 2014). An observable experiment, in its turn, is defined as the experiment in which the independent variables cannot possibly be controlled by the person or person setting the test. In the course of the experiment, the outcomes retrieved from testing a sample are inferred to the population under the analysis. For example, the differences in the choice of coping mechanisms among pregnant and not pregnant female patients with depression issues can be viewed as an observable experiment, since pregnancy is not the factor that the researcher can add or remove in the course of the experiment.

Justification

Indeed, a controlled trial among the identified members of the population can be defined as an observable experiment since the researcher cannot compel the participants either to get pregnant or to terminate their pregnancy. Instead, careful observations must take place so that the corresponding behavioral patterns and the development of coping mechanisms could be spotted (Welham, Gezan, & Clark, 2014).

Dependent Variables

Among the essential independent variables, pregnancy must be mentioned first. Similarly, the mood swings that the target audience is likely to be influenced by and caused by changes in the hormone balance. Likewise, the stress factors that women are exposed to in their workplace and home can be interpreted as independent variables.

Independent Variables

The coping patterns that the participants of the experiment develop when facing stressful situations can be considered the essential independent variable that will have to be observed in the course of the experiment. The study of the variable in question can be viewed as the primary goal of the experiment. More importantly, it is crucial to determine the effects of several essential variables, such as the current physical state (e.g., pregnancy and the co-morbid issues, as well as the absence thereof), etc., have on the development of new coping patterns, the change in the current ones, and other reactions that the target audience has.

Factors/Levels

According to the existing definition, experimental levels of the population consist of several essential factors that define the specifics of the experiment participants. In the identified study, the factors involve the marital status (e.g., married, not married, or divorced), the number of children that the participant already has, the relationships that she has with her colleagues (e.g., friendly, distanced, cold, conflicting, etc.), and other elements. Going into detail, one may assume that there are approximately three essential factors (i.e., the marital status, the type of relationships, and the existence of family support), each having two or three levels. It could be argued, however, that the addition of new levels is required to make the research more detailed (Hahs-Vaughn & Lomax, 2013).

Experimental Units

In the course of the study, the number of experimental units will hinge on the number of factors isolated in the course of the preliminary research. Thus, a detailed analysis of the ways, in which the coping strategies are shaped, can be identified (Lawless, 2014).

From Observable to Designed

In the identified scenario, the transformation from an observable to a designed type of experiment will require a change of research focus. Seeing that the designed experiment implies that the variables should be controlled by the researchers, it will be necessary to consider carrying out a study among non-pregnant women.

Reference List

Groebner, D. F., Shannon, P. W., & Fry, P. C. (2014). Analysis of variance. In Business statistics (9th ed.) (pp. 540-546). Upper Saddle River, NJ: Prentice Hall.

Hahs-Vaughn, D. L., & Lomax, R. G. (2013). An introduction to statistical concepts (3rd ed.). New York, NY: Routledge.

Lawless, J. F. (2014). Statistics in action: A Canadian outlook. Chicago, IL: CRC Press.

Welham, S. J., Gezan, S. A., & Clark, S. J. (2014). Statistical methods in biology: Design and analysis of experiments and regression. Chicago, IL: CRC Press.

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